PROJECT SUMMARY/ABSTRACT The percent of Gram-negative bacterial infections that are resistant to common antibiotics has increased at an alarming rate over the last decade, and there is now an acute need for the discovery of novel antibiotics effective against multidrug-resistant Gram-negative pathogens. The standard method of antibacterial discovery ? whole- cell screening of compound collections ? has met with repeated failure for Gram-negatives, and these failures have been traced to the fact that very few compounds in standard collections can penetrate the Gram-negative cell membranes and accumulate in these pathogens. Unfortunately, there has been scant information about the types of compounds that are competent for accumulation in Gram-negatives. Excitingly, we recently assessed a unique collection of >180 diverse compounds for their ability to accumulate in E. coli, trained a random forest classification model to analyze the results, and from this data we identified physicochemical properties important for accumulation and developed predictive guidelines for compound accumulation in E. coli. We then showed the utility of these guidelines by converting a Gram-positive-only antibiotic into a broad-spectrum agent. We now propose to develop tools that will allow us to fully define the physicochemical traits that enable compounds accumulation in three of the most concerning Gram-negative bacteria, carbapenem-resistant Enterobacteriaceae (CRE), drug-resistant Acinetobacter, and drug-resistant P. aeruginosa (to be referred to collectively as EAP pathogens). Specifically, we seek to develop novel tools in the area of chemical probes (compound collections), bacterial strains, and computational models. Using these tools in conjunction with our well-validated compound accumulation assay, we intend to define the physicochemical traits needed for compound accumulation in the EAP pathogens, including assessment of the influence of porins and efflux pumps, and the relative contribution of the outer and inner-membranes to blocking compound penetrance. Our predictive guidelines will be utilized to convert several high-value Gram-positive-only compounds into broad- spectrum antibiotics.